logical operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644 2.15 Chart Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Release 1.3.2 (continued from previous page) Out[8]: 0 True 1 False 2 dtype: boolean 2.15 Chart Visualization This section demonstrates visualization through charting. For information on visualization columns=list("ABCD ˓→")) In [7]: df = df.cumsum() In [8]: plt.figure(); In [9]: df.plot(); 2.15. Chart Visualization 647 pandas: powerful Python data analysis toolkit, Release 1.3.2 You can plot one

logical operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673 2.15 Chart Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Series([True, False, np.nan], dtype="boolean") & True Out[8]: 0 True 1 False 2 dtype: boolean 2.15 Chart Visualization This section demonstrates visualization through charting. For information on visualization index=pd.date_range("1/1/2000",␣ ˓→periods=1000)) In [4]: ts = ts.cumsum() In [5]: ts.plot(); 2.15. Chart Visualization 675 pandas: powerful Python data analysis toolkit, Release 1.3.3 If the index consists

logical operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674 2.15 Chart Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Series([True, False, np.nan], dtype="boolean") & True Out[8]: 0 True 1 False 2 dtype: boolean 2.15 Chart Visualization This section demonstrates visualization through charting. For information on visualization columns=list("ABCD")) In [7]: df = df.cumsum() In [8]: plt.figure(); In [9]: df.plot(); 2.15. Chart Visualization 677 pandas: powerful Python data analysis toolkit, Release 1.3.4 You can plot one

logical operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676 2.15 Chart Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . nan], dtype="boolean") & True Out[8]: 0 True 1 False 2 dtype: boolean {{ header }} 2.15 Chart Visualization This section demonstrates visualization through charting. For information on visualization go beyond the basics documented here. Note: All calls to np.random are seeded with 123456. 2.15. Chart Visualization 677 pandas: powerful Python data analysis toolkit, Release 1.4.2 2.15.1 Basic plotting:

logical operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676 2.15 Chart Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . nan], dtype="boolean") & True Out[8]: 0 True 1 False 2 dtype: boolean {{ header }} 2.15 Chart Visualization This section demonstrates visualization through charting. For information on visualization go beyond the basics documented here. Note: All calls to np.random are seeded with 123456. 2.15. Chart Visualization 677 pandas: powerful Python data analysis toolkit, Release 1.4.4 2.15.1 Basic plotting:

Boolean data type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684 2.2.15 Chart visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 686 2 nan], dtype="boolean") & True Out[8]: 0 True 1 False 2 dtype: boolean {{ header }} 2.2.15 Chart visualization Note: The examples below assume that you’re using Jupyter. This section demonstrates 0rc0 You can pass other keywords supported by matplotlib scatter. The example below shows a bubble chart using a column of the DataFrame as the bubble size. In [76]: df.plot.scatter(x="a", y="b", s=df["c"]

here (GH16157) • DataFrame.style.bar() now accepts two more options to further customize the bar chart. Bar alignment is set with align='left'|'mid'|'zero', the default is “left”, which is backward compatible; 0.20.3 You can pass other keywords supported by matplotlib scatter. Below example shows a bubble chart using a dataframe column values as bubble size. In [65]: df.plot.scatter(x='a', y='b', s=df['c']*200); formats.style.Styler at 0x10a311828> New in version 0.20.0 is the ability to customize further the bar chart: You can now have the df.style.bar be centered on zero or midpoint value (in addition to the already

here (GH16157) • DataFrame.style.bar() now accepts two more options to further customize the bar chart. Bar alignment is set with align='left'|'mid'|'zero', the default is “left”, which is backward compatible; 0.20.2 You can pass other keywords supported by matplotlib scatter. Below example shows a bubble chart using a dataframe column values as bubble size. In [65]: df.plot.scatter(x='a', y='b', s=df['c']*200); formats.style.Styler at 0x111564a58> New in version 0.20.0 is the ability to customize further the bar chart: You can now have the df.style.bar be centered on zero or midpoint value (in addition to the already

here (GH16157) • DataFrame.style.bar() now accepts two more options to further customize the bar chart. Bar alignment is set with align='left'|'mid'|'zero', the default is “left”, which is backward compatible; 0.21.1 You can pass other keywords supported by matplotlib scatter. Below example shows a bubble chart using a dataframe column values as bubble size. In [65]: df.plot.scatter(x='a', y='b', s=df['c']*200); formats.style.Styler at 0x1160e9b00> New in version 0.20.0 is the ability to customize further the bar chart: You can now have the df.style.bar be centered on zero or midpoint value (in addition to the already

0 You can pass other keywords supported by matplotlib scatter. The example below shows a bubble chart using a column of the DataFrame as the bubble size. In [68]: df.plot.scatter(x='a', y='b', s=df['c'] style.Styler at 0x7fb446e34748> New in version 0.20.0 is the ability to customize further the bar chart: You can now have the df.style.bar be centered on zero or midpoint value (in addition to the already Estimate plot using Gaussian kernels. Series.plot.line(**kwds) Line plot. Series.plot.pie(**kwds) Pie chart. pandas.Series.plot.area Series.plot.area(**kwds) Area plot. Parameters ‘**kwds‘ [optional] Additional